This document accompanies the thesis “Information diffusion in complex emergencies”. This files does not aim to be self containing. Instead it aims to give the reader an idea of which analysis have been performed during the research project.
This analysis follows 5 steps. These steps are:
The remainder of this document discusses the steps one by one.
Figure 1 shows information diffusion for 50 identical disasters. The only variable that was changed for these disasters was the time at which need chancing shocks took place. This figure show that the level of information diffusion is different for various timings of shocks. This behaviour illustrates how the information landschap is continous evolving based on amongst others the timing of shocks.
Figure 2 shows that the number of days worked increase as the number of shocks increase.
Figure 3 shows the effect of more shocks on the relief gap.
Figure 4 shows the effect of the number of shocks on the diffusion of information.
Figure 5 also shows the effect of the number of shocks but then on the diffusion of information per programme manager per day.
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Publication strategies that are focused on fast publication of inaccuracte information sharing lead to more information diffusion (figure 1) and smaller relief gaps (figure 2) with less effort (lower number of days worked).
Did expect the effect to be so strong (it is the most effective strategie).
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Focusing on the strategie hand over knowledge has no signification effect on information diffusion, the total relief gap or the total days worked.
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
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## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
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In contrast to focusing on handing over knowledge does the strategie hand over contacts slightly increase the information diffused and a small decrease of the relief gap and days worked.
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No supprising behavriour: Information diffusion is highest with a time-focused, local-based, high inter-organisations information sharing strategy.
comperhansive_str = sqldf("SELECT * FROM comperhansive_str WHERE shocks = 12")
boxplot_comperhansive_str = ggplot(comperhansive_str, aes(x=comperhansive_str$RS_publication_method, y= total_information_diffused/days_worked, group = (RS_publication_method), fill = (comperhansive_str$total_information_diffused/comperhansive_str$days_worked))) + geom_boxplot() + facet_grid(comperhansive_str$Willingness_focus ~ comperhansive_str$share_international_local) + labs(title = "Effects of eight comprehensive strategies on the diffusion of information", subtitle = " Share local programme managers", x = "", y = "Information diffusion per programme manager per day" )
print(boxplot_comperhansive_str)
ggsave("plots/3_1boxplot_comperhansive_str.png",boxplot_comperhansive_str)
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# Not in thesis
boxplot_comperhansive_str_inf_diff_absolute = ggplot(comperhansive_str, aes(x=comperhansive_str$RS_publication_method, y= total_information_diffused, group= (RS_publication_method))) + geom_boxplot() + facet_grid(comperhansive_str$Willingness_focus ~ comperhansive_str$share_international_local, ) + labs(title = "Effects of eight comprehensive strategies on the diffusion of information (absolute)", subtitle = " Share local programme managers",
x = "", y = "Information diffusion" )
print(boxplot_comperhansive_str_inf_diff_absolute)
boxplot_comperhansive_str_gap = ggplot(comperhansive_str, aes(x=comperhansive_str$RS_publication_method, y= total_gap_over_time, group= (RS_publication_method))) + geom_boxplot() + facet_grid(comperhansive_str$Willingness_focus ~ comperhansive_str$share_international_local, ) + labs(title = "Effects of eight comprehensive strategies on the relief gap", subtitle = " Share local programme managers",
x = "", y = "Relief gap" )
print(boxplot_comperhansive_str_gap)
boxplot_comperhansive_str_days = ggplot(comperhansive_str, aes(x=comperhansive_str$RS_publication_method, y= days_worked, group= (RS_publication_method))) + geom_boxplot() + facet_grid(comperhansive_str$Willingness_focus ~ comperhansive_str$share_international_local, ) + labs(title = "Effects of eight comprehensive strategies on days worked", subtitle = " Share local programme managers",
x = "", y = "Days worked" )
print(boxplot_comperhansive_str_days)
ggsave("plots/3_2boxplot_comperhansive_str_gap.png",boxplot_comperhansive_str_gap)
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ggsave("plots/3_3boxplot_comperhansive_str_days.png",boxplot_comperhansive_str_days)
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boxplot_effect_distributions_shocks_inf_diff = ggplot(data=effect_distributions_shocks, aes(x=shocks, y=total_information_diffused/days_worked, group = shocks)) + facet_grid( rows = vars(effect_distributions_shocks$distribution_accuracy)) + geom_boxplot()
print(boxplot_effect_distributions_shocks_inf_diff)
ggsave("plots/4_2boxplot_effect_distributions_shocks_inf_diff.png",boxplot_effect_distributions_shocks_inf_diff,width = 28, height = 40, dpi = 120)
boxplot_effect_distributions_shocks_gap = ggplot(data=effect_distributions_shocks, aes(x=shocks, y=total_gap_over_time, group = shocks)) + facet_grid( rows = vars(effect_distributions_shocks$distribution_accuracy)) + geom_boxplot()
print(boxplot_effect_distributions_shocks_gap)
ggsave("plots/4_3boxplot_effect_distributions_shocks_gap.png",boxplot_effect_distributions_shocks_gap,width = 28, height = 40, dpi = 120)
boxplot_effect_distributions_shocks_days = ggplot(data=effect_distributions_shocks, aes(x=shocks, y= days_worked, group = shocks)) + facet_grid( rows = vars(effect_distributions_shocks$distribution_accuracy)) + geom_boxplot()
print(boxplot_effect_distributions_shocks_days)
ggsave("plots/4_4boxplot_effect_distributions_shocks_days .png",boxplot_effect_distributions_shocks_days ,width = 28, height = 40, dpi = 120)
boxplot_Ind_str_publication_method_distribution_inf_diff = ggplot(Ind_str_publication_method_distribution, aes(x=RS_publication_method, y=total_information_diffused/days_worked, group= (RS_publication_method))) + facet_grid( rows = vars(Ind_str_publication_method_distribution$distribution_accuracy)) +
geom_boxplot() +
labs(title = "Effect changing publication method on information diffusion for different accuracy distributions",
x = "Publication method", y = "Information diffusion per programme manager per day")
print(boxplot_Ind_str_publication_method_distribution_inf_diff)
ggsave("plots/4_5boxplot_Ind_str_publication_method_distribution_inf_diff.png", boxplot_Ind_str_publication_method_distribution_inf_diff,width = 10, height = 12, dpi = 120)
boxplot_Ind_str_publication_method_distribution_gap = ggplot(Ind_str_publication_method_distribution, aes(x=RS_publication_method, y=total_gap_over_time, group= (RS_publication_method))) + facet_grid( rows = vars(Ind_str_publication_method_distribution$distribution_accuracy)) +
geom_boxplot() +
labs(title = "Effect changing publication method on relief gap for different accuracy distributions",
x = "Publication method", y = "Relief gap")
print(boxplot_Ind_str_publication_method_distribution_gap)
ggsave("plots/4_6boxplot_Ind_str_publication_method_distribution_gap.png", boxplot_Ind_str_publication_method_distribution_gap ,width = 10, height = 12, dpi = 120)
boxplot_Ind_str_publication_method_distribution_days = ggplot(Ind_str_publication_method_distribution, aes(x=RS_publication_method, y=days_worked, group= (RS_publication_method))) + facet_grid( rows = vars(Ind_str_publication_method_distribution$distribution_accuracy)) +
geom_boxplot() +
labs(title = "Effect changing publication method on days worked for different accuracy distributions",
x = "Publication method", y = "Days worked")
print(boxplot_Ind_str_publication_method_distribution_days)
ggsave("plots/4_7boxplot_Ind_str_publication_method_distribution_days.png", boxplot_Ind_str_publication_method_distribution_days ,width = 10, height = 12, dpi = 120)
It is problematic that the number of assessments increases (but the accuracy stays the same -> benefit of increasing speed).
boxplot_sensitivity_assessment_length_inf_diff = ggplot(sensitivity_assessment_length, aes(x=assessment_length , y= total_information_diffused/days_worked, group= assessment_length)) + geom_boxplot() + labs(title = "Sensitivity analysis of the assessment length variable on information diffusion", x = "Assessment length", y = "Information diffusion per programme manager per day")
print(boxplot_sensitivity_assessment_length_inf_diff)
ggsave("plots/5_1boxplot_sensitivity_inf_diff.png",boxplot_sensitivity_assessment_length_inf_diff)
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boxplot_sensitivity_assessment_length_gap = ggplot(sensitivity_assessment_length, aes(x=assessment_length , y= total_gap_over_time, group= assessment_length)) + geom_boxplot() + labs(title = "Sensitivity analysis of the assessment length variable on relief gap",
x = "Assessment length", y = "Relief gap")
print(boxplot_sensitivity_assessment_length_gap)
ggsave("plots/5_2boxplot_sensitivity_gap.png",boxplot_sensitivity_assessment_length_gap)
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boxplot_sensitivity_assessment_length_days = ggplot(sensitivity_assessment_length, aes(x=assessment_length , y= days_worked, group= assessment_length)) + geom_boxplot() + labs(title = "Sensitivity analysis of the assessment length variable on days worked",
x = "Assessment length", y = "days worked")
print(boxplot_sensitivity_assessment_length_days)
ggsave("plots/5_3boxplot_sensitivity_days.png",boxplot_sensitivity_assessment_length_days)
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